From the state of the art, a large number of models for modeling the cylinder charge of an internal combustion engine are known. The internal combustion engine which is to be modeled often comprises a fully variable valve lift, and the modeling is performed under consideration of the suction tube pressure and by a model-based approach, wherein, in the state of the art, a linear approximation of the cylinder charge on the basis of the suction tube pressure is highly inaccurate, particularly in case of supercharged engines and suction engines with high interior or exterior exhaust gas recirculation. Already known is the linear modeling in accordance with a straight line m*x+b, wherein x represents the suction tube pressure. In case of a large valve overlap, however, this approach is too inaccurate.
EP-A-1 431 548 describes a device for setting a vacuum and a valve lift for control of internal exhaust gas recirculation (EGR), wherein a differentiation is made between a cold and a warm engine for controlling the internal EGR.
WO-A-2006/024397 describes a method for model-based determination of the air mass flowing into the cylinder combustion chamber of an internal combustion engine during a suction phase. Determination of the fresh air mass flowing into the cylinder chamber during the suction phase is performed by means of various load part models wherein, at least via a first load part model with purely linear functionality, a first fresh air portion will be determined and, via a second load part model with non-linear functionality, a second fresh air portion will be determined.
EP-A-1 234 958 describes a method for control of the adjustment of the inlet phase shifter in continuous variable valve lift (CVVL) engines in accordance with the operational state and respectively the current range of the characteristic map.
DE-C-102 24 213 describes a method for charge control of an internal combustion engine. In this method, two actuators, arranged behind each other in the suction duct and each controlling the air volume flow through the suction duct, can be used in mutual attunement for charge control. Using the measurement and model values, an adjustment of the model is performed, wherein the desired positions for the two actuators are determined with the aid of a model that is inverted relative to the adjusted model. Said model comprises two part models, there being provided a first part model wherein the model value for the air volume flow is computed from the measurement value of the suction tube pressure and the actual position of the first actuator, and a second part model wherein the model value for the suction tube pressure is computed from the measurement value of the air volume flow and the actual position of the second actuator. Prior to computation of the second part model, the first part model will be adjusted, wherein an adjustment parameter is determined that will be considered in the second part model.
DE-A-102 27 466 describes a method and a device for determining the cylinder charge of an internal combustion engine. With the aid of a first air volume model, the air mass sucked in by a cylinder will be computed. This first air mass model may be based exclusively on the sensor models of the air mass actuators, such as e.g. the position of the air mass actuator and respectively the variable valve lift, or there may be additionally considered the measured pressure in the suction tube. The first air mass model will be adjusted via a second air mass model. The air mass computed in the second model is based on the mixture composition of the exhaust gas which is determined on the basis of a lambda value, and on the supplied fuel mass. In this manner, engine-individual tolerances are said to be compensated for.
DE-A-101 13 538 describes a control device and control method comprising a real-time controller with neuronal adaption for control of internal combustion engines. In said control device and respectively method, an online adaption to non-linear variation is performed. Online adaption is carried out with the aid of neuronal correction device. A network type suitable for the neuronal correction device is a LOLIMOT network.
DE-A-199 14 910 describes a hybrid model for modeling a total process in a vehicle, consisting of respectively one physical and one neuronal part model. The total process, e.g. the filling of the cylinders, will be divided into part processes which are described by various part models and then will be combined into a total model. In an internal combustion engine with variable valve lift, the base charge shall be determined via a physical model. However, the influence of a camshaft shift, i.e. the rotational offset of the camshaft relative to the crankshaft, shall be described via the neuronal network. It is said to be an advantage of DE-A-199 14 910 that, by use of a base model with physical process description, the portion of the neuronal part model can be restricted. In this manner, it shall be guaranteed that the total model will not indicate an implausible exponential behavior.
EP-B-0 877 309 describes virtual vehicle sensors based on neuronal networks which are trained by data generated through simulation models. During the training of the network, the various connections together with the appertaining weightings will be determined. With the aid of a polynomial model arranged downstream of the neuronal network, a sensor output can be generated. However, according to the description, the type of a neuronal regression model is not restricted to the use of polynomials. Because of the tendency that the interpolation of polynomials will be affected by large errors, it is preferred to select non-polynomial functions.
DE-A-197 06 750 describes a method for mix control in an internal combustion engine, and devices for performing said method. The method comprises a learning process in which both stationary and dynamic operational states will be included. In this manner, there is described a trainable mix control which compares the actually existing mix ratio to a desired value and, in case of a deviation therefrom, will adapt the stored control information to the effect that, in future passes through the same or a similar operating point, a reduced deviation will be obtained. Thus, the neuronal network is trained online.
DE-C-44 21 950 describes a device for the diagnosing and controlling of internal combustion and electric motors. Further described is the use of a neuronal network which, using measurement data of sensors, will drive a control system for controlling the motor.
DE-C-195 47 496 describes a method for control of internal combustion engines. Particularly, there is presented a method for determination of an air mass flow which is sucked by the cylinders of an internal combustion engine and which serves as a base for the metering of the fuel by a control device comprising a disturbance value monitoring unit of the non-linear type. The purpose of this monitoring approach resides in estimating the air mass flow into the cylinders by learning the volumetric efficiency of the engine with the aid of methods of artificial intelligence. To this end, use can be made e.g. of a neuronal network which initially has a large number of values that will influence the volumetric efficiency. Among these, there are a suction tube pressure, a rotary speed but also, possibly, valve control times or other parameters.